Detecting personality and emotion traits in crowds from video sequences

Favaretto, Rodolfo Migon; Knob, Paulo; Musse, Soraia Raupp; Vilanova, Felipe; Costa, Ângelo Brandelli

doi:10.1007/s00138-018-0979-y

Cited by 20 publications

(13 citation statements)

References 36 publications

(60 reference statements)

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“…In discussing growth opportunities using biometric data, we focus on eye tracking (e.g., Pieters and Wedel 2004) and emotion detection (e.g., Liu et al 2018), which can be implemented at scale. Methods are being developed and improved to detect emotions from individuals in a crowd (e.g., Favaretto et al 2019). However, we have yet to see these tools being applied to large crowds.…”

Section: Incorporating Biometric Data Into Customer Acquisitionmentioning

confidence: 99%

Capturing Marketing Information to Fuel Growth

Du¹,

Netzer²,

Schweidel³

et al. 2020

Journal of Marketing

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Marketing is the functional area primarily responsible for driving the organic growth of a firm. In the age of digital marketing and big data, marketers are inundated with increasingly rich data from an ever-expanding array of sources. Such data may help marketers generate insights about customers and competitors. One fundamental question remains: How can marketers wrestle massive flows of existing and nascent data resources into coherent, effective growth strategies? Against such a backdrop, the Marketing Science Institute has made “capturing information to fuel growth” a top research priority. We begin by discussing the streetlight effect—an overreliance on readily available data due to ease of measurement and application—as contributing to the disconnect between marketing data growth and firm growth. We then use the customer equity framework to structure our discussion of six areas where we see substantial under-tapped opportunities—incorporating social network and biometric data in customer acquisition, trend and competitive interaction data in customer development, and unstructured and causal data in customer retention. We highlight challenges that obstruct firms from realizing such data-driven growth opportunities and how future research may help overcome those challenges.

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Section: Incorporating Biometric Data Into Customer Acquisitionmentioning

confidence: 99%

Capturing Marketing Information to Fuel Growth

Du¹,

Netzer²,

Schweidel³

et al. 2020

Journal of Marketing

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“…Related to emotion analysis, as we presented in [27], we proposed a way to map the OCEAN dimensions of each pedestrian in OCC Emotion model. This mapping is described in Table II.…”

Section: First and Second Dimensions: Data Extraction Crowd Typmentioning

confidence: 99%

Emotion, Personality and Cultural Aspects in Crowds: towards a Geometrical Mind

Favaretto¹

2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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In this work we proposed a computational model to extract pedestrian characteristics from video sequences. The proposed model considers a series of characteristics of the pedestrians and the crowd, such as number and size of groups, distances, speeds, among others, and performs the mapping of these characteristics in personalities, emotions and cultural aspects, considering the Cultural Dimensions of Hofstede (HCD), the Big-Five Personality Model (OCEAN) and the OCC Emotional Model. The main hypothesis is that there is a relationship between so-called intrinsic human variables (such as emotion) and the way people behave in space and time. As one of the main contributions, four large dimensions of geometric characteristics (Big4GD) were proposed: I - Physical, II - Personal and Emotional, III - Social and IV - Cultural, which seek to describe the behavior of pedestrians and groups in the crowd. The GeoMind tool was developed for the purpose of detecting the four geometric dimensions from video sequences. In addition, several analyzes were carried out with the purpose of validating the proposed model, from comparing results with the literature, including the comparison of spontaneous multitudes from several countries and controlled experiments involving Fundamental Diagrams.

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“…Other approaches such as Affective Neuroscience postulate, from an evolutionary perspective, consider other groups of emotions such as fear, rage/anger and sadness/panic [30]. In [17] we proposed a way to detect pedestrian emotions in videos, based on OCC emotion model. To detect the emotions of each pedestrian, we used OCEAN as inputs, as proposed by Saifi [32].…”

Section: Related Workmentioning

confidence: 99%

A Software to Detect OCC Emotion, Big-Five Personality and Hofstede Cultural Dimensions of Pedestrians from Video Sequences

Favaretto,

Araujo,

Musse

et al. 2019

Preprint

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This paper presents a video analysis application to detect personality, emotion and cultural aspects from pedestrians in video sequences, along with a visualizer of features. The proposed model considers a series of characteristics of the pedestrians and the crowd, such as number and size of groups, distances, speeds, among others, and performs the mapping of these characteristics in personalities, emotions and cultural aspects, considering the Cultural Dimensions of Hofstede (HCD), the Big-Five Personality Model (OCEAN) and the OCC Emotional Model. The main hypothesis is that there is a relationship between so-called intrinsic human variables (such as emotion) and the way people behave in space and time. The software was tested in a set of videos from different countries and results seem promising in order to identify these three different levels of psychological traits in the filmed sequences. In addition, the data of the people present in the videos can be seen in a crowd viewer.

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Detecting personality and emotion traits in crowds from video sequences

Cited by 20 publications

References 36 publications

Capturing Marketing Information to Fuel Growth

Capturing Marketing Information to Fuel Growth

Emotion, Personality and Cultural Aspects in Crowds: towards a Geometrical Mind

A Software to Detect OCC Emotion, Big-Five Personality and Hofstede Cultural Dimensions of Pedestrians from Video Sequences

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